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import json import imas,os,datetime,sys import getpass import numpy as np from imas import imasdef db = 'data_access_tutorial' shot=99357 run=1 #creates the Data Entry object 'data_entry', a kind of handler of the pulse file with sho, run, belonging to database 'data_access_tutorial' of the current user, using the MDS+ backend data_entry = imas.DBEntry(imasdef.MDSPLUS_BACKEND, db, shot, run, user_name=getpass.getuser()) #data_entry = imas.DBEntry(imasdef.HDF5_BACKEND, db, shot, run, user_name=getpass.getuser()) # Open save data_entry to data base # Tries to open data_entry op = data_entry.open() #if fails, creates one if op[0]<0: cp=data_entry.create() if cp[0]==0: print("data entry created") elif op[0]==0: print("data entry opened") # Fetched data with open("data/data_jet_hrts_99357.json") as json_file: hrts=json.load(json_file) x_coord=np.array(hrts['TE']['x']) # no. of space points nb_points = len(x_coord) #no of time slices nb_slices=len(hrts['TE']['time']) #here, we can perform some read/write operations using the get/put() operations #... #creating the 'thomson_scattering' auxiliary IDS and initializing it thomson = imas.thomson_scattering() #creates a 'thomson scattering' IDS thomson.ids_properties.homogeneous_time=1 #setting the homogeneous time (mandatory) thomson.ids_properties.comment='IDS created for testing the IMAS Data Access layer' #setting the ids_properties.comment attribute #thomson.time=np.array([0.]) #the time(vector) basis must be not empty if homogeneous_time==1 otherwise an error will occur at runtime # since all data is available we can save whole time vector at once thomson.time=np.array(hrts['TE']['time']) thomson.ids_properties.creation_date = datetime.datetime.now().strftime("%y-%m-%d") thomson.channel.resize(nb_points) for j in range(nb_points): thomson.channel[j].t_e.data.resize(1) thomson.channel[j].t_e.data_error_upper.resize(1) thomson.channel[j].n_e.data.resize(1) thomson.channel[j].n_e.data_error_upper.resize(1) # python interface accepts only numpy arrays to be saved in ids te_data = np.array(hrts['TE']['data']) #2D dte_data = np.array(hrts['DTE']['data'])#2D ne_data = np.array(hrts['NE']['data'])#2D dne_data = np.array(hrts['DNE']['data'])#2D z_data = np.array(hrts['Z']['data']) #1D r_data = np.array(hrts['TE']['x']) #1D for j in range(nb_points): thomson.channel[j].position.r=r_data[j] thomson.channel[j].position.z=z_data[j] thomson.channel[j].t_e.data=te_data[:,j] thomson.channel[j].t_e.data_error_upper=dte_data[:,j] thomson.channel[j].n_e.data=ne_data[:,j] thomson.channel[j].n_e.data_error_upper=dne_data[:,j] data_entry.put(thomson) |
Note that we used only one command 'data_entry.put(thomson)' . Since we had all data available at once we didn't need to use put and putSlice commands to save slice by slice. However, the two approaches should be equivalent and provide the same ids. The first one is faster as it saving is done only once.
If the data was saved with MDSPLUS backend , the shot number is included in the data files names and is saved in the following folder:
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